Research Track session

With the popularity of smartphones and mobile devices, mobile application (a.k.a. “app”) markets have been growing exponentially in terms of number of users and downloads. App developers spend considerable effort on collecting and exploiting user feedback to improve user satisfaction, but suffer from the absence of effective user review analytics tools. To facilitate mobile app developers discover the most “informative” user reviews from a large and rapidly increasing pool of user reviews, we present “AR-Miner” — a novel computational framework for App Review Mining, which performs comprehensive analytics from raw user reviews by (i) first extracting informative user reviews by filtering noisy and irrelevant ones, (ii) then grouping the informative reviews automatically using topic modeling, (iii) further prioritizing the informative reviews by an effective review ranking scheme, (iv) and finally presenting the groups of most “informative” reviews via an intuitive visualization approach. We conduct extensive experiments and case studies on four popular Android apps to evaluate AR-Miner, from which the encouraging results indicate that AR-Miner is effective, efficient and promising for app developers.

Programming languages evolve over time, adding additional language features to simplify common tasks and make the language easier to use. For example, the Java Language Specification has four editions and is currently drafting a fifth. While the addition of language features is driven by an assumed need by the community (often with direct requests for such features), there is little empirical evidence demonstrating how these new features are adopted by developers once released. In this paper, we analyze over 31k open-source Java projects representing over 9 million Java files, which when parsed contain over 18 billion AST nodes. We analyze this corpus to find uses of new Java language features over time. Our study gives interesting insights, such as: there are millions of places features could potentially be used but weren't; developers convert existing code to use new features; and we found thousands of instances of potential resource handling bugs.

A frequently occurring usage of program elements in a programming language and software libraries is called a usage pattern. In JavaScript (JS) Web applications, JS usage patterns in their source code have special characteristics that pose challenges in pattern mining. They involve nested data objects with no corresponding names or types. JS functions can be also used as data objects. JS usages are often cross-language, inter-procedural, and involve control and data flow dependencies among JS program entities and data objects whose data types are revealed only at run time due to dynamic typing in JS. This paper presents JSModel, a novel graph-based representation for JS usages, and JSMiner, a scalable approach to mine inter-procedural, data-oriented JS usage patterns. Our empirical evaluation on several Web programs shows that JSMiner efficiently detects more JS patterns with higher accuracy than a state-of-the-art approach. We conducted experiments to show JSModel's usefulness in two applications: detecting anti-patterns (buggy patterns) and documenting JS APIs via pattern skeletons. Our controlled experiment shows that the mined patterns are useful as JS documentation and code templates.

Identifying repetitive code changes benefits developers, tool builders, and researchers. Tool builders can automate the popular code changes, thus improving the productivity of developers. Researchers can better understand the practice of code evolution, advancing existing code assistance tools and benefiting developers even further. Unfortunately, existing research either predominantly uses coarse-grained Version Control System (VCS) snapshots as the primary source of code evolution data or considers only a small subset of program transformations of a single kind - refactorings. We present the first approach that identifies previously unknown frequent code change patterns from a fine-grained sequence of code changes. Our novel algorithm effectively handles challenges that distinguish continuous code change pattern mining from the existing data mining techniques. We evaluated our algorithm on 1,520 hours of code development collected from 23 developers, and showed that it is effective, useful, and scales to large amounts of data. We analyzed some of the mined code change patterns and discovered ten popular kinds of high-level program transformations. More than half of our 420 survey participants acknowledged that eight out of ten transformations are relevant to their programming activities.